{
 "nbformat": 4,
 "nbformat_minor": 2,
 "metadata": {
  "language_info": {
   "name": "python",
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "version": "3.7.3-final"
  },
  "orig_nbformat": 2,
  "file_extension": ".py",
  "mimetype": "text/x-python",
  "name": "python",
  "npconvert_exporter": "python",
  "pygments_lexer": "ipython2",
  "version": 2,
  "kernelspec": {
   "name": "python271664bitenvvirtualenv8836d05012704695b9eba33d763507b5",
   "display_name": "Python 2.7.16 64-bit ('env': virtualenv)"
  }
 },
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from matplotlib import rc\n",
    "import pandas as pd\n",
    "\n",
    "# plot style\n",
    "sns.set_style('whitegrid')\n",
    "sns.set_style({'font.family': 'Times New Roman'})\n",
    "\n",
    "sns.set_context(\"paper\", font_scale=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "total = pd.read_csv(\"data/total.csv\", index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "All journals' success rate: 0.42024253731343286\n"
    }
   ],
   "source": [
    "journals = total[total['type']==\"JOURNALS\"]\n",
    "print(\"All journals' success rate: \"+ str(journals.success.sum()*1./len(journals)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from helpers import fix_name, fix_name2\n",
    "\n",
    "journals.name = journals.name.apply(fix_name)\n",
    "journals.name = journals.name.apply(fix_name2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "policy = pd.read_csv(\"data/journal-policy-survey.txt\", sep ='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>name</th>\n      <th>link</th>\n      <th>type</th>\n      <th>level</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>American Journal of Political\\nScience (AJPS)</td>\n      <td>https://ajps.org/ajps-verification-policy/</td>\n      <td>verification</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>British Journal of\\nPolitical Science</td>\n      <td>https://www.cambridge.org/core/services/aop-fi...</td>\n      <td>required</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>American Political\\nScience Review</td>\n      <td>https://www.apsanet.org/APSR-Submission-Guidel...</td>\n      <td>required</td>\n      <td>3</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
      "text/plain": "                                            name  \\\n0  American Journal of Political\\nScience (AJPS)   \n1          British Journal of\\nPolitical Science   \n2             American Political\\nScience Review   \n\n                                                link          type  level  \n0         https://ajps.org/ajps-verification-policy/  verification      5  \n1  https://www.cambridge.org/core/services/aop-fi...      required      3  \n2  https://www.apsanet.org/APSR-Submission-Guidel...      required      3  "
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "policy.name = policy.name.apply(fix_name2)\n",
    "policy.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "journal_policy = pd.merge(journals,policy,on='name')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "1971"
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(journal_policy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "agg_total = pd.read_csv(\"data/agg_total.csv\", index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "ag_journals = agg_total[agg_total['type']==\"JOURNALS\"]\n",
    "ag_journals = ag_journals.drop(ag_journals[ag_journals.success.isnull()].index)\n",
    "\n",
    "ag_journals.name = ag_journals.name.apply(fix_name)\n",
    "ag_journals.name = ag_journals.name.apply(fix_name2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "Aggregated all journals' success rate: 0.4729381443298969\n"
    }
   ],
   "source": [
    "print(\"Aggregated all journals' success rate: \"+ str(ag_journals.success.sum()/len(ag_journals)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "ag_journal_policy = pd.merge(ag_journals,policy,on='name')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "barWidth = 0.8\n",
    "\n",
    "def get_stats(df):\n",
    "    res = df.groupby(\n",
    "        ['success', 'name']).size().reset_index().pivot(columns='success', index='name', values=0)\n",
    "\n",
    "    res.fillna(0.0, inplace=True)\n",
    "    res['total'] = [i+j for i,j in zip(res[1],res[0])]\n",
    "\n",
    "    res['s'] = [i / j * 100 for i,j in zip(res[1], res['total'])]\n",
    "\n",
    "    res = res.sort_values(by=['total'], ascending=False)\n",
    "    return res\n",
    "\n",
    "import matplotlib.patches as patches\n",
    "from helpers import *\n",
    "\n",
    "def make_plot(plt, temp, pos):\n",
    "    pal = sns.color_palette(\"nipy_spectral\")\n",
    "    colors = {\"no policy\": pal[4], \"encouraged\": pal[3], \"required\": pal[2], \"reviewed\": pal[1], \"verification\": pal[0]} \n",
    "    \n",
    "    names = list(temp.name)\n",
    "    r= range(0,len(temp['s']))\n",
    "    plt.bar(r, temp['s'], color=temp.type.map(colors), alpha=1., edgecolor='black', width=barWidth)\n",
    "\n",
    "    global labels, handles\n",
    "    labels = list(colors.keys())\n",
    "    if pos == 2:\n",
    "        plt.set_xticks(r)\n",
    "        plt.set_xticklabels(names, rotation=90) #, fontsize=14)\n",
    "    else:\n",
    "        plt.set_xticks([])\n",
    "        handles = [patches.Rectangle((0,0),1,1, color=colors[label]) for label in labels]\n",
    "        \n",
    "    plt.set_ylim(0,77)\n",
    "\n",
    "    temp['names'] = temp.index\n",
    "    temp = temp.reset_index(drop=True)\n",
    "\n",
    "    temp.s = temp.s.round(1)\n",
    "    temp.total = temp.total.astype(int)\n",
    "\n",
    "    autolabel(plt.patches, plt)\n",
    "    autolabel_count_boxed(plt.patches, plt, temp)\n",
    "\n",
    "    plt.yaxis.grid(True) # Hide the horizontal gridlines\n",
    "    plt.xaxis.grid(False) # Show the vertical gridlines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": "<Figure size 344.16x309.6 with 2 Axes>"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(2,1, figsize=(4.78,4.3)) #, gridspec_kw={\"height_ratios\": (.45, .55)})\n",
    "\n",
    "j2 = get_stats(ag_journals).head(11)\n",
    "j2 = pd.merge(j2.reset_index(),policy,on='name').sort_values(by=['type','name'], ascending=False)\n",
    "make_plot(axs[0], j2, pos=1)\n",
    "axs[0].set_title(\"Aggregated success rate per journal Dataverse collection\") #, size=20)\n",
    "\n",
    "j1 = get_stats(journals)\n",
    "j1 = pd.merge(j1.reset_index(),policy,on='name').sort_values(by=['type','name'], ascending=False)\n",
    "j1 = j1.drop(j1[~j1.name.isin(j2.name)].index)\n",
    "make_plot(axs[1], j1, pos=2)\n",
    "axs[1].set_title(\"Success rate (file-level) per journal Dataverse collection\") #, size=20)\n",
    "\n",
    "legend = fig.legend(\n",
    "            handles, labels, frameon=False, #facecolor='white', \n",
    "            #edgecolor='gray', \n",
    "            fontsize=8,\n",
    "            title=\"Data sharing policy type\", ncol=5,\n",
    "            bbox_to_anchor=(0.03, -.025, .94, .14),\n",
    "            mode=\"expand\", #borderaxespad=0.,\n",
    "            loc = 'center',\n",
    "            )\n",
    "\n",
    "fig.tight_layout()\n",
    "\n",
    "#plt.savefig(\"publisher_temp1.pdf\", bbox_inches = \"tight\", dpi=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}